Future Sales Prediction

Importing the libraries

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

Reading the dataset

data=pd.read_csv("salesPredictionDataset.csv")
print(data.head())

      TV  Radio  Newspaper  Sales
0  230.1   37.8       69.2   22.1
1   44.5   39.3       45.1   10.4
2   17.2   45.9       69.3   12.0
3  151.5   41.3       58.5   16.5
4  180.8   10.8       58.4   17.9

Check if the dataset contains any null values

print(data.isnull().sum())

TV           0
Radio        0
Newspaper    0
Sales        0
dtype: int64

this dataset doesn’t have any null values. let’s visualize the relationship between the amount spent on advertising on TV and units sold:

import plotly.express as px
import plotly.graph_objects as go
figure = px.scatter(data_frame = data, x="Sales",
                    y="TV", size="TV", trendline="ols")
figure.show()

Now visualising the relationship between the amount spent on advertising in newspaper and unit sold:

figure=px.scatter(data_frame=data, x="Sales", y="Newspaper", size="Newspaper", trendline="ols")
figure.show()

Visualising the relationship between amount spent on advertising on radio and unit sold:

figure = px.scatter(data_frame = data, x="Sales", y="Radio", size="Radio", trendline="ols")
figure.show()

Out of all the amounts spent on advertising, Advertising on TV results in more sales of products. Let's see the correlation of all the columns with sales column

correlation = data.corr()
print(correlation["Sales"].sort_values(ascending=False))

Sales        1.000000
TV           0.901208
Radio        0.349631
Newspaper    0.157960
Name: Sales, dtype: float64

Splitting the data into training and testing sets:

x = np.array(data.drop(["Sales"], 1))
y = np.array(data["Sales"])
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)

C:\Users\Prakhar\AppData\Local\Temp/ipykernel_1576/1754818733.py:1: FutureWarning:

In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only

Now in this section, We will train a machine learning model to predict the future sales of a product.

model = LinearRegression()
model.fit(xtrain, ytrain)
print(model.score(xtest, ytest))

0.9059011844150826

let’s input values into the model according to the features we have used to train it and predict how many units of the product can be sold based on the amount spent on its advertising on various platforms:

#features = [[TV, Radio, Newspaper]]
features = np.array([[5600006.1, 37.8, 69.2]])
print(model.predict(features))

[305261.07915204]